Details

Autor(en) / Beteiligte

• Matthews, Jonathan M
• Schuster, Brooke
• Kashaf, Sara Saheb
• Liu, Ping
• Ben-Yishay, Rakefet
• Ishay-Ronen, Dana
• Izumchenko, Evgeny
• Shen, Le
• Weber, Christopher R
• Bielski, Margaret
• Kupfer, Sonia S
• Bilgic, Mustafa
• Rzhetsky, Andrey
• Tay, Savaş
• Schneidman-Duhovny, Dina

Titel

OrganoID: A versatile deep learning platform for tracking and analysis of single-organoid dynamics

Ist Teil von

• PLoS computational biology, 2022-11, Vol.18 (11), p.e1010584-e1010584

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• Organoids have immense potential as ex vivo disease models for drug discovery and personalized drug screening. Dynamic changes in individual organoid morphology, number, and size can indicate important drug responses. However, these metrics are difficult and labor-intensive to obtain for high-throughput image datasets. Here, we present OrganoID, a robust image analysis platform that automatically recognizes, labels, and tracks single organoids, pixel-by-pixel, in brightfield and phase-contrast microscopy experiments. The platform was trained on images of pancreatic cancer organoids and validated on separate images of pancreatic, lung, colon, and adenoid cystic carcinoma organoids, which showed excellent agreement with manual measurements of organoid count (95%) and size (97%) without any parameter adjustments. Single-organoid tracking accuracy remained above 89% over a four-day time-lapse microscopy study. Automated single-organoid morphology analysis of a chemotherapy dose-response experiment identified strong dose effect sizes on organoid circularity, solidity, and eccentricity. OrganoID enables straightforward, detailed, and accurate image analysis to accelerate the use of organoids in high-throughput, data-intensive biomedical applications.